Abstract

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Physics-based computer models of storm surge can be implemented with a wide range of fidelity due to the nature of the system, though the danger posed by surge makes greater fidelity highly desirable. However, such models and their high-dimensional outputs tend to come at great computational cost, which can make highly detailed studies prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model that is able to address these issues. Based upon the data-augmentation technique, model parameters are estimated via Monte Carlo expectation-maximization algorithm and prediction is made in a computationally efficient way when input designs across different fidelity levels are not nested. With this methodology, the high-fidelity storm surges can be generated much more quickly in coastal flood studies, and hence can facilitate the risk assessment of storm surge hazards.